Refining of segmental boundaries in speech waveforms using contextual-dependent models

ABSTRACT

A method and apparatus are provided for refining segmental boundaries in speech waveforms. Contextual acoustic feature similarities are used as a basis for clustering adjacent phoneme speech units, where each adjacent pair phoneme speech units include a segmental boundary. A refining model is trained for each cluster and used to refine boundaries of contextual phoneme speech units forming the clusters.

BACKGROUND OF THE INVENTION

The present invention relates to language processing systems. Inparticular, the present invention relates concatenative text-to-speech(TTS) systems where speech output is generated by concatenating smallstored speech units or segments one by one in series.

Ascertaining segmental boundaries for adjacent speech units used in acorpus-based concatenative TTS system is important in realizingnaturalness in generated speech output from such systems. Priortechniques include manually labeling such boundaries. Although thistechnique is reliable, it is nevertheless very laborious and timeconsuming, making such a technique impractical to be applied to a largespeech corpus.

Accordingly, there has developed a need to provide an automatic speechsegmentation approach with comparable accuracy to human experts. Such asystem and method would be particularly helpful when speech units areobtained from a large speech corpus. One segmentation method is referredto as “forced alignment” and is widely used in the training stage of HMMbased Automatic Speech Recognition (ASR) systems. However, in performingforced alignment, boundary marks are to some extent under-estimated asViterbi algorithm is targeted to match the wave stream to the wholelabeled speech state sequence in a criterion minimizing the globaldistance. However, boundaries obtained in this manner are often notidentical to the best splicing points between speech units. Thus,post-refinement is often performed to search for the most suitablelocations for boundaries. The post-refinement technique uses a smallamount of manually labeled boundaries for learning the characteristicsof human-preferred boundary marks.

Various refining techniques have been used to refine the boundarylocations. These techniques include using Gaussian Mixture Models (GMM),Hidden Markov Model (HMM), Neural Networks (NN) and Maximum LikelihoodProbabilities (MLPs) to portray the boundary property. Some techniqueshave included classifying speech units by phonemic context, such asVowel, Nasals, Liquids etc, where a refining model was trained for eachgroup. However, classification is coarse such that the phonemic contextwithin the same group may vary greatly. For example, /i/ and /u/, whichare often clustered into the Vowel group, have quite different formanttrajectories. Modeling them with the same refining model causes a lossin precision. An ideal solution is to train an individual model for eachpair of speech unit boundaries. However, there are normally notsufficient manually labeled boundaries for training so many individualmodels.

Although various approaches have been tried to refine segmentalboundaries for TTS speech units, none have achieved superior results,and thus improvements are continually needed.

SUMMARY OF THE INVENTION

A method and apparatus are provided for segmenting boundaries in speechwaveforms. In one aspect, refining models are generated that are basedon training data of known boundary locations. In another aspect, therefining models are used to automatically segment speech waveforms.

Generally, the training data of speech waveforms with known boundarylocations is processed to obtain multi-frame acoustic featurepseudo-triphone representations of a plurality of pseudo-triphones inthe speech data. Each pseudo-triphone includes a boundary location, afirst phoneme speech unit preceding the boundary location and a secondphoneme speech unit following the boundary location.

The multi-frame acoustic feature pseudo-triphone representations areclustered as a function of acoustic similarity to provide a plurality ofclusters. A refining model is trained for each cluster.

The set of refining models can be used to segment a second set of dataof speech waveforms with initial boundary locations of adjacent phonemespeech units contained therein. First, pseudo-triphones are identifiedin the second set of data along with the corresponding refining modelsfor each of the pseudo-triphones. Using the refining model for eachcorresponding pseudo-triphone a new boundary location is ascertainedthat is more accurate than the initial boundary.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a general computing environment in whichthe present invention may be practiced.

FIG. 2 is a block diagram for a system for creating a set of refiningmodels and use thereof in an automatic boundary segmentation system.

FIG. 3 is a flow diagram of a method of creating a set of refiningmodels.

FIG. 4 is a pictorial representation of a speech waveform and acousticfeatures extracted therefrom.

FIG. 5 is flow diagram of a method for automatic boundary segmentation.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The present invention relates to a system and method for refiningsegmental boundaries of speech units used in concatenative TTS systems.However, prior to discussing the present invention in greater detail,one illustrative environment in which the present invention can be usedwill be discussed.

FIG. 1 illustrates an example of a suitable computing system environment100 on which the invention may be implemented. The computing systemenvironment 100 is only one example of a suitable computing environmentand is not intended to suggest any limitation as to the scope of use orfunctionality of the invention. Neither should the computing environment100 be interpreted as having any dependency or requirement relating toany one or combination of components illustrated in the exemplaryoperating environment 100.

The invention is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, and the like.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Thoseskilled in the art can implement the description and/or figures hereinas computer-executable instructions, which can be embodied on any formof computer readable media discussed below.

The invention may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both locale and remotecomputer storage media including memory storage devices.

With reference to FIG. 1, an exemplary system for implementing theinvention includes a general purpose computing device in the form of acomputer 110. Components of computer 110 may include, but are notlimited to, a processing unit 120, a system memory 130, and a system bus121 that couples various system components including the system memoryto the processing unit 120. The system bus 121 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a locale bus using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) locale bus, and Peripheral ComponentInterconnect (PCI) bus also known as Mezzanine bus.

Computer 110 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computer 110 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 100. Communication media typicallyembodies computer readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier WAVor other transport mechanism and includes any information deliverymedia. The term “modulated data signal” means a signal that has one ormore of its characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, FR,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer readable media.

The system memory 130 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 131and random access memory (RAM) 132. A basic input/output system 133(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 110, such as during start-up, istypically stored in ROM 131. RAM 132 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 120. By way o example, and notlimitation, FIG. 1 illustrates operating system 134, applicationprograms 135, other program modules 136, and program data 137.

The computer 110 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 1 illustrates a hard disk drive 141 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 151that reads from or writes to a removable, nonvolatile magnetic disk 152,and an optical disk drive 155 that reads from or writes to a removable,nonvolatile optical disk 156 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 141 is typically connectedto the system bus 121 through a non-removable memory interface such asinterface 140, and magnetic disk drive 151 and optical disk drive 155are typically connected to the system bus 121 by a removable memoryinterface, such as interface 150.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 1, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 110. In FIG. 1, for example, hard disk drive 141 is illustratedas storing operating system 144, application programs 145, other programmodules 146, and program data 147. Note that these components can eitherbe the same as or different from operating system 134, applicationprograms 135, other program modules 136, and program data 137. Operatingsystem 144, application programs 145, other program modules 146, andprogram data 147 are given different numbers here to illustrate that, ata minimum, they are different copies.

A user may enter commands and information into the computer 110 throughinput devices such as a keyboard 162, a microphone 163, and a pointingdevice 161, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 120 through a user input interface 160 that is coupledto the system bus, but may be connected by other interface and busstructures, such as a parallel port, game port or a universal serial bus(USB). A monitor 191 or other type of display device is also connectedto the system bus 121 via an interface, such as a video interface 190.In addition to the monitor, computers may also include other peripheraloutput devices such as speakers 197 and printer 196, which may beconnected through an output peripheral interface 190.

The computer 110 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer180. The remote computer 180 may be a personal computer, a hand-helddevice, a server, a router, a network PC, a peer device or other commonnetwork node, and typically includes many or all of the elementsdescribed above relative to the computer 110. The logical connectionsdepicted in FIG. 1 include a locale area network (LAN) 171 and a widearea network (WAN) 173, but may also include other networks. Suchnetworking environments are commonplace in offices, enterprise-widecomputer networks, intranets and the Internet.

When used in a LAN networking environment, the computer 110 is connectedto the LAN 171 through a network interface or adapter 170. When used ina WAN networking environment, the computer 110 typically includes amodem 172 or other means for establishing communications over the WAN173, such as the Internet. The modem 172, which may be internal orexternal, may be connected to the system bus 121 via the user-inputinterface 160, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 110, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 1 illustrates remoteapplication programs 185 as residing on remote computer 180. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

It should be noted that the present invention can be carried out on acomputer system such as that described with respect to FIG. 1. However,the present invention can be carried out on a server, a computer devotedto message handling, or on a distributed system in which differentportions of the present invention are carried out on different parts ofthe distributed computing system.

As indicated above, the present invention relates to a system and methodfor refining segmental boundaries of phoneme speech units used inconcatenative TTS systems. In general, contextual acoustic featuresimilarities are used as a basis for clustering adjacent phoneme speechunits based on phoneme context, where each adjacent pair of phonemespeech units include a segmental boundary. A refining model is thentrained for each cluster and used to refine boundaries of contextualphoneme speech units forming the clusters.

Before describing the refinement technique in detail, a briefdescription of segmental boundaries and the contextual-dependentboundary model used herein may be helpful. The change in the speechwaveform across a segmental boundary is determined by the phoneme speechunits on the left and right sides of the boundary. As used herein, aboundary can be represented by a “pseudo-triphone” in the form of X-B-Y,where B represents a boundary, X represents the phoneme speech unit tothe left of the boundary, and Y represents the phoneme speech unit tothe right of it. Here “triphone” should not be considered restrictive orlimiting as pertaining to only context dependent phonemes, but rather“pseudo-triphone” is used as a means for describing the context existingabout the boundary, where the phoneme speech unit includes phones orphonemes, but is not limited thereto. In particular, the phoneme speechunit can be more complex than a single phoneme. For example, thedependent contextual model of a pseudo-triphone can be applied tosyllables such as used in Chinese or other languages. For instance, thepseudo-triphone for the segmental boundary between syllable /tian/ and/qi/ in Chinese is /n-B-q/.

Theoretically, there are NX*NY possible such pseudo-triphones, where NXis the number of all possible phoneme speech units X, while NY is thenumber of all possible phoneme speech units Y, given a particularlanguage and the complexity of the phoneme speech unit being used informing the phoneme speech unit database of the concatenative TTSsystem. NX and NY are not necessarily the same. However not all of themappear in the speech corpus to be labeled.

FIG. 2 illustrates in block diagram form a system 200 for obtaining aset of segmental boundary refining models adapted for use in obtainingboundary locations automatically from a large corpus. FIG. 3 illustratesa method 300 for obtaining the set of boundary refining models. Store202 schematically illustrates a training corpus comprising acousticspeech waveforms with corresponding phoneme speech unit segmentalboundaries that are known to be accurate. Commonly, such boundaries areobtained by manually labeling the boundary location with respect to thespeech waveform.

Acoustic feature generator 204 receives speech waveforms and thecorresponding labeled boundaries and generates multi-frame acousticfeature pseudo-triphone representations 206 for each of thepseudo-triphones comprising the speech waveforms, which is indicatedgenerally at step 302 in FIG. 3.

FIG. 4 schematically illustrates a pseudo-triphone waveform 400 having aleft phoneme speech unit 401, a boundary 402 and a right phoneme speechunit 404. A corresponding multi-frame acoustic feature pseudo triphonerepresentation 406 for waveform 400 can be represented by 2N+1 frames ofacoustic features, m-dimension each, which are extracted from timet_(−N) to t_(N), where t₀ is the location of the boundary 402, t_(−N) tot⁻¹ are N frames to the left of the boundary and t₁ to t_(N) are Nframes to the right of the boundary. Extraction of the acoustic featuresis illustrated at 304 in step 302. In one embodiment as illustrated inFIG. 4, a frame step 408 is set to be larger than a frame size 410 sothat consecutive frames are not overlapped and the correspondingmulti-frame acoustic feature pseudo triphone representation 406 containsmore information about the boundary 402. In another embodiment, havingthe frame size 410 be larger than the frame step 408 so that consecutiveframe overlap exists can provide improved results. An example of frameoverlap can be 5 milliseconds.

After extraction, the acoustic features are combined at step 306. The2N+1 frames acoustic features can form a (2N+1)*m dimension matrix orcan be put together to form a (2N+1)*m-dimension super vector 406(herein illustrated) for that boundary 402. The acoustic features can beany of the widely used features such as MFCCs (Mel Frequency CepstralCoefficients), LPCs (Linear Prediction Coefficients), LSPs (LineSpectral Pair)/LSF (Line Spectral Frequencies), etc. In one exemplaryimplementation, 5 frames of 39-dimension vectors (13-dimension MFCCs,13-deimension ΔMFCCs and 13 dimension ΔΔMFCCs) are used. The frame sizeis 25 ms and the frame step is 30 ms. The 5 frames of acoustic vectorsform a 195-dimension super vector to represent the correspondingboundary 402. Principal Component Analysis (PCA), Independent ComponentAnalysis (ICA) or Linear Discriminant Analysis (LDA) approaches can beused to reduce the dimensions of the multi-frame acoustic feature pseudotriphone representation 406, if desired. A set of multi-frame acousticfeature pseudo triphone representations provided by acoustic featuregenerator 204 is indicated at 206 in FIG. 2.

For modeling each type of boundaries precisely, training a refiningmodel for each type of pseudo-triphone is desired. However, since thereare normally limited manually labeled data available for training, it isnot realistic to train a reliable model for each and everypseudo-triphone. Therefore, a clustering module 208 receives the set ofmulti-frame acoustic feature pseudo triphone representations 206 toclassify and thereby provide a set of clustered, or categorized,multi-frame acoustic feature pseudo triphone representations 210, eachcluster typically comprising a plurality of multi-frame acoustic featurepseudo triphone representations. Clustering is indicated at step 308 inFIG. 3. In one embodiment, a Classification and Regression Tree (CART)is used to cluster similar multi-frame acoustic feature pseudo triphonerepresentations into the same category or cluster. Those unseenmulti-frame acoustic feature pseudo triphone representations can bemapped to a suitable leaf node or cluster as well.

Since the segmental boundaries are treated as a pseudo-triphone, themodel clustering procedure is the same as what is done in trainingacoustic models for phoneme speech units. In fact, the same question setcan be used as well.

As appreciated by those skilled in the art, use of a Classification andRegression Tree is one form of clustering technique that can be used.Other clustering techniques by way of example and not limitation includeSupport Vector Machine (SVM), Neural network (NN), or VectorQuantization (VQ).

By using CART or other clustering techniques, it becomes possible tocontrol the number of nodes (clusters) created, for example, accordingto the amount of training data available, for instance, by setting athreshold for the Minimum Training Instances (MTI) per leaf node orcluster greater than one. Experiments were conducted for a training setwith 5,000 pseudo-triphone instances and 20,000 pseudo-triphoneinstances respectively. Through adjusting the MTI per leaf node, CARTsof different scales were obtained. As the MTI decreases, the number ofleaf nodes on the CART (also the number of refining models discussedbelow) increases. It was found that, when training with the 20,000 set,the accuracy of the refinement drops if the MTI is set to values largerthan 40 and the accuracy is almost unchanged for all other settings.However, when the train set is reduced to 5,000 samples, the accuracy ofrefinement increases as the MTI decreases until it reaches 10. Thisimplies that the accuracy of refinement will increase when morecontextual-dependent models are used as long as a minimum number ofinstances for training a reliable GMM are used.

In addition, the more training data available, the more leaf nodes(clusters) are formed and the more precise models are obtained. In oneexperiment, the MTI is set to 10 and it was found that as the size oftraining set exceeds 5,000, the rate of performance improvement startsto slow down. Of course, more training data is still helpful. However,in the experiment, the curve becomes saturated after the train setreaches 30,000. Therefore, it appears at least 5,000 correct boundaries(approximately 250 utterances) are recommended for training the refiningmodels. However, the approach also works when not much training data isavailable.

Having obtained the set of multi-frame acoustic feature pseudo triphonerepresentations 210, a refining model is trained for each leaf node orcluster by refining model generator 212 at step 310, which provides aset of refining boundary models 214. Each refining model is then usedfor refining the boundaries of the pseudo-triphones belonging to thatleaf node or cluster at step 312 for another corpus.

A cluster of boundaries (pseudo-triphones) can be modeled by HiddenMarkov Model (HMM), Neural Networks (NN) or MLPs. In the exemplaryimplementation, a Gaussian Mixture Model (GMM) is used to model the mostlikely locations of boundaries for each cluster. The number of mixturesis adjustable. Although a plurality of Gaussians can be used, in oneembodiment, using only one Gaussian provided the best results. Thismight be because that the transitions at boundaries in each cluster aresimilar to each other so that one Gaussian is good enough to model thedistribution of the features. In some instances, increasing the numberof mixtures may have a detrimental effect on boundary accuracy. Thereason for this may be that, when the number of instances on some leafnodes is small, the parameters of multiple Gaussian mixtures cannot beestimated reliably.

Once the training is completed, automatic refinement of all boundariesin a large or simply another corpus can start. In FIG. 2, automaticboundary segmentation is performed by boundary segmenting module 216,which receives as an input the set of refining modules 214 and a corpusor store 218 of acoustic speech waveforms with corresponding phonemespeech unit segmental boundaries that will be refined. In oneembodiment, corpus 218 can be obtained from a speech recognition system220 operated to perform forced alignment over the corpus 218. Typically,such systems have not been found to be very accurate in ascertainingphoneme speech unit boundaries; however by using the set of refiningmodels 214 accuracy has been significantly improved without manualintervention.

Generally, for a specific boundary to be refined, the optimal locationof boundary is assumed to be in the vicinity of the initial boundary,i.e. a more suitable boundary is to be searched in a certain rangearound the initial one (that obtained by the forced alignment or anyother methods). Normally, a small frame step is used in the refiningstage in order to get precise locations of boundaries. The smaller theframe step is, the more precise the optimal boundary will be, however,at the cost of more calculations. In one exemplary implementation, astep of 1 millisecond (ms) is used and the search range is from 70 ms tothe left of the initial boundary to 70 ms to the right of the initialboundary.

FIG. 5 illustrates a method 500 for refining an initial boundary of apseudo-triphone in corpus 218. At step 502, an initial boundary of apseudo-triphone is ascertained. Acoustic features are first extractedfor frames in the search range at step 504. In the exemplaryimplementation mentioned above, 141 frames of 195-dimension vectors areextracted. A leaf node on the CART or other cluster is found by queryingto its corresponding pseudo-triphone at step 506. As a result, thepre-trained refining model 508 attached to the leaf node or cluster isfound from the set of refining models 214. The likelihood for each framein the search range is the calculated at step 510 using the pre-trainedrefining model 508 (e.g. a GMM in the illustrative example) and theframe that has the maximum likelihood is outputted as indicated at 512as the optimal location for the boundary for the pseudo-triphone underconsideration. All of boundaries of the pseudo-triphones in the corpus218 can be refined in this manner to provide a set 224.

In summary, a system and method have been described that provide apost-refining method with fine contextual-dependent refining models forthe auto-segmentation task of boundaries of adjacent phoneme speechunits. The refining model is trained with a super feature vectorextracted from multiple, preferably, evenly spaced frames near theboundary, which is used to describe the waveform evolution across aboundary. A clustering technique such as CART is used to clusteracoustically similar boundaries, so that the refining model for eachleaf node is reliably trained with a small amount of limited manuallylabeled boundaries.

The system and method provides accurate boundaries for phoneme speechunits automatically given a small training set of accurately locatedboundaries and a larger corpus upon which other phoneme speech units canbe obtained. For instance, the system of FIG. 2 and the methods of FIGS.3 and 5 can used to provide customized phoneme speech units for a givenspeaker. In particular, a speaker can provide a first, relatively smallset of training data utterances (e.g. 250 utterances) that is accuratelyanalyzed to locate phoneme speech unit boundaries for corpus 202. A setof refining models 214 can then be obtained as discussed above andapplied to a larger corpus 218 from the speaker to automatically obtainphoneme speech unit boundaries in order to quickly develop a corpus ofphoneme speech units that can be used in a TTS system designed toemulate the given speaker. The system of FIG. 2 can be implemented asdiscussed above with the environment of FIG. 1, operating on a singlecomputer, local area network or across a wide area network such as theInternet where component modules and stores are remote from one another.

Although the present invention has been described with reference toparticular embodiments, workers skilled in the art will recognize thatchanges may be made in form and detail without departing from the spiritand scope of the invention.

1. A method of ascertaining phoneme speech unit boundaries of adjacentspeech units in speech data, the method comprising: receiving trainingdata of speech waveforms with known boundary locations of phoneme speechunits contained therein; processing the speech waveforms to obtainmulti-frame acoustic feature pseudo-triphone representations of aplurality of pseudo-triphones in the speech data, each pseudo-triphonecomprising a boundary location, a first phoneme speech unit precedingthe boundary location and a second phoneme speech unit following theboundary location; clustering the multi-frame acoustic featurepseudo-triphone representations as a function of acoustic similarity ina plurality of clusters; training a refining model for each cluster;receiving a second set of data of speech waveforms with initial boundarylocations of adjacent phoneme speech units contained therein;identifying pseudo-triphones in the second set of data and correspondingrefining models for each of the pseudo-triphones; and using the refiningmodel for each corresponding pseudo-triphone for the second set of datato locate a new boundary location different than the initial boundaryand provide output indicating the new boundary locations.
 2. The methodof claim 1 wherein clustering comprises maintaining a minimum number ofmulti-frame acoustic feature pseudo-triphone representations greaterthan one in each cluster.
 3. The method of claim 1 wherein clusteringcomprises controlling a number of clusters created.
 4. The method ofclaim 1 wherein clustering comprises using a Classification andRegression Tree clustering technique.
 5. The method of claim 1 whereinclustering comprises using a Support Vector Machine (SVM) clusteringtechnique.
 6. The method of claim 1 wherein clustering comprises using aNeural network (NN) clustering technique.
 7. The method of claim 1wherein clustering comprises using a vector quantization (VQ) clusteringtechnique.
 8. The method of claim 1 wherein processing the speechwaveforms to obtain multi-frame acoustic feature pseudo-triphonerepresentations comprises forming a multi-dimensional matrix or vectorbased on a number of frames of speech waveform data adjacent to theknown boundary.
 9. The method of claim 8 wherein forming amulti-dimensional matrix or vector comprises reducing the number ofdimensions.
 10. The method of claim 1 wherein training a refining modelfor each cluster comprises forming a Gaussian Mixture Model to model themost likely locations of a boundary for each cluster.
 11. The method ofclaim 10 wherein forming a Gaussian Mixture Model to model the mostlikely locations of a boundary for each cluster comprises obtaining onlya single Gaussian component.
 12. The method of claim 1 wherein traininga refining model for each cluster comprises forming a Neural Networkmodel to model the most likely locations of a boundary for each cluster.13. The method of claim 1 wherein training a refining model for eachcluster comprises forming a Hidden Markov Model to model the most likelylocations of a boundary for each cluster.
 14. The method of claim 1wherein training a refining model for each cluster comprises forming aMaximum Likelihood Probability model to model the most likely locationsof a boundary for each cluster.
 15. A computer-readable storage mediumhaving computer-executable instructions for processing speech data, thecomputer-readable medium comprising: an acoustic feature generatoradapted to receive training data of speech waveforms with known boundarylocations of phoneme speech units contained therein and generatemulti-frame acoustic feature pseudo-triphone representations of aplurality of pseudo-triphones in the training data, each pseudo-triphonecomprising a boundary location, a first phoneme speech unit precedingthe boundary location and a second phoneme speech unit following theboundary location; a clustering module adapted to receive themulti-frame acoustic feature pseudo-triphone representations of theplurality of pseudo-triphones and cluster the representations based onacoustic similarity; and a refining module generator adapted to operateon each cluster of representations and generate a statistical modeltherefor indicative of the location of the boundary for each cluster.16. The computer-readable storage medium of claim 15 wherein theclustering module comprises Classification and Regression Treeclustering module.
 17. The computer-readable storage medium of claim 16wherein the clustering module is adapted to maintain a minimum number ofmulti-frame acoustic feature pseudo-triphone representations greaterthan one in each cluster.
 18. The computer-readable storage medium ofclaim 16 wherein the clustering module is adapted to control a number ofclusters created.
 19. The computer-readable storage medium of claim 15wherein the clustering module comprises a Support Vector Machine (SVM)clustering module.
 20. The computer-readable storage medium of claim 19wherein the clustering module is adapted to maintain a minimum number ofmulti-frame acoustic feature pseudo-triphone representations greaterthan one in each cluster.
 21. The computer-readable storage medium ofclaim 19 wherein the clustering module is adapted to control a number ofclusters created.
 22. The computer-readable storage medium of claim 15wherein the clustering module comprises a Support Vector Machine (SVM)clustering module.
 23. The computer-readable storage medium of claim 22wherein the clustering module is adapted to maintain a minimum number ofmulti-frame acoustic feature pseudo-triphone representations greaterthan one in each cluster.
 24. The computer-readable storage medium ofclaim 22 wherein the clustering module is adapted to control a number ofclusters created.
 25. The computer-readable storage medium of claim 15wherein the clustering module comprises a vector quantization (VQ)clustering module.
 26. The computer-readable storage medium of claim 25wherein the clustering module is adapted to maintain a minimum number ofmulti-frame acoustic feature pseudo-triphone representations greaterthan one in each cluster.
 27. The computer-readable storage medium ofclaim 25 wherein the clustering module is adapted to control a number ofclusters created.
 28. The computer-readable storage medium of claim 15wherein acoustic feature generator is adapted to form amulti-dimensional matrix or vector based on a number of frames of speechwaveform data adjacent to the known boundary.
 29. The computer-readablestorage medium of claim 28 wherein the refining module generator isadapted to form a Gaussian Mixture Model to model the most likelylocations of a boundary for each cluster.
 30. The computer-readablestorage medium of claim 29 wherein the refining module generator isadapted to form a Gaussian Mixture Model having only a single Gaussiancomponent to model the most likely locations of a boundary for eachcluster.
 31. The computer-readable storage medium of claim 15 whereinthe refining module generator is adapted to form a Neural Network modelto model the most likely locations of a boundary for each cluster. 32.The computer-readable storage medium of claim 15 wherein the refiningmodule generator is adapted to form a Hidden Markov Model to model themost likely locations of a boundary for each cluster.
 33. Thecomputer-readable storage medium of claim 15 wherein the refining modulegenerator is adapted to form a Maximum Likelihood Probability model tomodel the most likely locations of a boundary for each cluster.
 34. Thecomputer-readable storage medium of claim 15 and further comprising: aboundary segmentation module adapted to receive the statistical modelfor each cluster of representations and a second set of data of speechwaveforms with initial boundary locations of adjacent phoneme speechunits contained therein and using the statistical models obtain newboundary locations for the adjacent phoneme speech units.